* [Zero-Dim] support input 0D Tensor as scalar attribute for some api

* fix doc

* standard api

* fix sparse bugs

* fix xpu bugs, test=kunlun

* remove hard code for custom unittest

* open ci, test=kunlun

* deal with conflict

* [Zero-Dim] support input 0D Tensor for sundary api

* fix comment

* Standardise batch norm

* standardize conv3d and depwise_conv2d

* fix ci bugs

* standard_api

* add hardtanh

* add extra attr property set

* add type_info for all context

* add onednn context to all context

* fix context compile error

* simplify conv kernel args

* pass runtime attr into dev_ctx

* fix marco error

* clear conv_grad_kernel extra args

* merge conv_grad_grad into conv_grad

* clear conv2d_grad_grad extra attrs

* clear yaml and eager extra attr

* fix conv1d error

* change to thread local

* fix npu compile failed

* try to fix windows compile failed

* add conv2d onednn phi kernel

* fix ci bugs (#36)

* fix compile bugs (#38)

* fix extra input transform bug (#39)

* support dynamic created attr (#40)

* reset extra info gen code

* rm conv_grad_grad kernel

* reimpl pass attr adapting

* add int attr support

* remove vector inputnames creating

* fix map at error

* Update paddle/phi/kernels/onednn/conv_grad_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* remove useless extra attrs

* replace mkldnn_engine by onednn_engine
Co-authored-by: NYuanRisheng <yuanrisheng@baidu.com>
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* standard api

* fix ci bugs

* fix ci bugs

* fix ce bugs

* add unittest for einsum-v2-trace and diagonal

* repeat labels.

* einsum support repeated labels.

* forward is ok for diagonal and undiagonalized.
TODO: check backward is ok by our theorem.

* backward is ok!

* fix by PR suggestions.

* fix ci error

* fix ci error

* fix ci warning

* fix indices

* namespace modify

* update by comment

* [Zero-Dim] support input 0D Tensor for unary api

* fix CI

* add yaml entry for rnn and rrnn_grad, move infershape function for rnn_grad to phi infer meta

* WIP: move rnn kernrl to phi

* Change the code generation to avoid converting from intializer list to tuple of heterogeneous types.
This is only triggered when an api has intermediate outputs, and the result of the outputs are of heterogeneous types.

* fix the bug that when none in a vector of tensors requires gradient, the conversion to InferShapeContext to InferMetaContext (a.k.a. BuildInferMetaContext) produces errorous results.

* fix ci bugs

* fix ci bugs

* fix ci bugs

* modify code according comment
Co-authored-by: Nchenfeiyu <chenfeiyu@baidu.com>

* remove invoke yaml

* fix ci bugs

* sparse infer_meta

* move sum

* fix ci bugs

* fix ci bugs

* fix set_lod bugs

* fix infershape bugs

* fix ci bugs

* fix ci unittest bug

* fix ci bugs

* perfect code

* update code according comment

* add unittest

* fix ci bugs

* fix return order error and duplicate results with specific inputs

* fix split bug in static mode

* modify code style

* modify code style

* add unit test for split

* add operator<< for BuildStrategy

* add fake_coalesce

* fit allreduce mode for new_exe

* remove dubeg code

* follow comments

* add fusion dir and fuse_softmax_mask kernel

* remove fusion kernel dir

* migrate infershape

* fix code errror

* add save kernel

* add save_sr_kernel

* remove original save_op

* add save gpu kernel

* remove combine kernel

* add port.h include

* add save selected rows test

* remove useless kernel.h

Adapt tensor output_size for conv2d_transpose and depthwise_conv2d_transpose

* add compile-time infermeta logic for stack infermeta.

* add unittest for stack infermeta where -1 exists in shapes.

* remove backward changes.

* fix amp kernel

* update to remove PADDLE_WITH_XPU macro

* [OpAttr]ksize of pool2d support Tensor type

* fix unittest

* add unittest

* [OpAttr]output_size of unpool support Tensor type

* fix coverage

* fix contain_var

* fix coverage